Learning spatio-temporal statistics from the environment in recurrent networks

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PI: Shouval, Harel Zeev (contact); Brunel, Nicolas

Email: Harel.Shouval@uth.tmc.edu

Institution: University of Texas Health Science Center Houston

Title: Learning spatio-temporal statistics from the environment in recurrent networks

  • Networks with fixed connectivity, storing fixed point attractors: A firing rate model with a learning rule that is constrained by in vivo data in inferior temporal cortex and produces attractor dynamics. https://github.com/ulisespereira/AttractorDynamics
  • Networks of with fixed connectivity, storing sequences: Firing rate and spike-based models with fixed connectivity that can store sequences of activity
  • Networks with plastic synapses that store the order of sequences: A firing rate model that stores sequences of activity through a simple temporally asymmetric unsupervised learning rule. Weblink: in preparation
  • Network models that can learn the order and duration of sequences using a columnar architecture:  A network model with a pre-specified columnar architecture in which different layers of a micro-column have different dynamics can learn to represent sequences of inputs as well as the duration of each element.
  • Calcium-based synaptic plasticity model: A calcium-based synaptic plasticity model that fits hippocampal slice data for various concentrations of extracellular calcium, time differences between pre and post-synaptic firing, firing frequency, and number of spikes in a burst.
  • Unified calcium-based model for unsupervised and reinforcement learning: A calcium-based plasticity model that also incorporates a role for neuro-modulators in synaptic plasticity. This model can unify previously proposed calcium-based models of unsupervised learning and also reinforcement-based models with eligibility traces. Such a model fits data of cortical slices with and without the application of neuromodulators.

Grant #: EB022891 

Status: Completed



BRAIN Math Project - Shouval.pptx

Link to Data/Model Reuse abstract, [Link] 



2021 Brain PI Meeting


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